A support vector machine (SVM) is a powerful tool for data classification and is often used for data mining operations. Classification is achieved by identifying a linear or nonlinear separating surface in the input space of a data set. The separating surface distinguishes between two classes of elements forming an extremely large data set. Advantageously, the separating surface depends only on a subset of the original data. This subset of data, which is used to define the separating surface, constitutes a set of support vectors.
To enhance performance of an SVM classifier, it is desirable to make the set of support vectors that defines the separating surface as small as possible, e.g., by reducing the set of input features in the case of a linear SVM classifier, or reducing a set of kernel functions in the case of a nonlinear SVM classifier. Applications such as fraud detection, credit evaluation, gene expression, and medical diagnosis or prognosis, for example, may present an input space with thousands, or even millions, of data points. The ability to suppress less useful data and select a reduced set of meaningful support vectors can greatly enhance the performance of the SVM classifier, in terms of computational resources, speed, and accuracy.